There are more model level defence strategies: 1) Reconstructing attacked model strategy. This strategy aims to retrain the trained attacker model with some benign samples to alleviate the backdoor attacks. 2) Synthesis trigger defencing strategy. It attempts to perform outlier detection on the DL models by reconstructing either the specific trigger [1]}, [2]} or the trigger distribution [3]}, [4]}. 3) Diagnosing attacked model through meta-classifier. This method applies certain pre-trained classifiers to identify the potentially infected models and prevent deploying them [5]}, [6]}. 4) Unlearning the infected model. The unlearning strategy first detects the malicious behavior and then defend against the backdoor attack by performing reverse learning through utilizing the implicit hypergradient [7]} or modifying the loss function [8]}. Moreover, the comprehensive backdoor attack surveys can be found in [9]} and  [10]}.
